Preoperatively molecular staging with CM10 ProteinChip and SELDI-TOF-MS for colorectal cancer patients

Objectives： To detect the serum proteomic patterns by using SELDI-TOF-MS （surface enhanced laser desorption/ ionization-time of flight-mass spectrometry） technology and CM10 ProteinChip in colorectal cancer （CRC） patients, and to evaluate the significance of the proteomic patterns in the tumour staging of colorectal cancer. Methods： SELDI-TOF-MS and CM10 ProteinChip were used to detect the serum proteomic patterns of 76 patients with colorectal cancer, among them, 10 Stage Ⅰ, 19 Stage Ⅱ, 16 Stage Ⅲ and 31 Stage Ⅳ samples. Different stage models were developed and validated by support vector machines, disctiminant analysis and time-sequence analysis. Results： The Model Ⅰ formed by 6 protein peaks （m/z： 2759.58, 2964.66, 2048.01, 4795.90, 4139.77 and 37761.60） could be used to distinguish local CRC patients （Stage Ⅰ and Stage Ⅱ） from regional CRC patients （Stage Ⅲ） with an accuracy of 86.67% （39/45）. The Model Ⅱ formed by 3 protein peaks （m/z： 6885.30, 2058.32 and 8567,75） could be used to distinguish locoregional CRC patients （Stage Ⅰ, Stage Ⅱ and Stage Ⅲ） from systematic CRC patients （Stage IV） With an accuracy of 75.00% （57/76）. The Model Ⅲ could distinguish Stage Ⅰ from Stage Ⅱ with an accuracy of 86.21% （25/29）. The Model Ⅳ could distinguish Stage Ⅰ from Stage Ⅲ with accuracy of 84.62% （22/26）. The Model Ⅴ could distinguish Stage Ⅱ from Stage Ⅲ with accuracy of 85.71% （30/35）. The Model Ⅵ could distinguish Stage Ⅱ from Stage Ⅳ with accuracy of 80.00% （40/50）. The Model Ⅶ could distinguish Stage Ⅲ from Stage Ⅳ with accuracy of 78.72% （37/47）. Different stage groups could be distinguished by the two-dimensional scattered spots figure obviously. Conclusion： This method showed great success in preoperatively determining the colorectal cancer stage of patients.

Generic Tabu Search

The Multiple Sequence Alignment problem is considered to be an NP-Hard problem, requiring initially a specific encoding schema and design, as for any other of its siblings, to implement and run any of the main categories of heuristic. This paper intends to discuss our proposed generic implementation of the Tabu Search algorithm, a heuristic procedure proposed by Fred Glover to solve discrete combinatorial optimization problems. In this research, we try to coordinate and synchronize different designs/implementations discussed in many literatures, with some of the references mentioned in this paper. The basic idea is to avoid that the search for best solutions stops when a local optimum is found, by maintaining a list of non-acceptable or forbidden (taboo) solutions/costs, called Tabu list or Short-Term Memory (STM). In our algorithm, we attempt to add some executions tracing functionalities in order to help later analysis for initial parameters tuning. On the other hand, we propose to include the concept of a list called Long-Term Memory (LTM), so that some of the best solutions found so far can be saved, for search diversification.

Generic Simulated Annealing

One of the many problems that are considered to be NP-Hard is the Multiple Sequence Alignment one that initially requires, as for any other of its siblings, a specific encoding schema and design of the main functionalities of the heuristics algorithm being implemented and executed. This paper intends to discuss our proposed generic implementation of the Simulated Annealing, inspired for the procedure of cooling and shaping methods of metals. In our algorithm, we attempted to add some executions tracing functionalities in order to help later analysis for initial parameters tuning. On another hand, we also tried to get closer in our attempt to mimic the cooling of metals, but giving it an option to run under different cooling schedules. We proposed a few schedules that seemed to be studied and/or used in many algorithm implementations.

Flexible Traceable Generic Genetic Algorithm

This document elaborates on the generic implementation one of the main heuristics algorithms verified through its quick application to a biology problem requiring to find out an optimal sequences tree topology. In order to solve this problem, categorized as Non-Polynomial Hard (NP-Hard), “to minimize differences between given (leaf) and/or derived (parent) sequences”, many popular methods are used. “The higher the number of given sequences is, the more advisable and efficient it would be to go towards heuristics as they would provide a close-enough solution faster, as for instance genetic algorithms amongst others do. Thus, as part of a larger research in Heuristics and phylogenies, this paper aims to suggest a generic advanced flexible implementation of the Genetic Algorithm verified by a “general way to encode the problem into instances of different heuristic algorithms” as mentioned in our first reference below. The proposed algorithm will also present a chronology traceability feature for further analysis and potential improvements.